Fix #10: Remove hardcoded secret from history Update readme

README.md

@@ -1,3 +1,17 @@
+# Keyword-Based News Fetcher
+
+## Description
+A Python module that fetches the top news articles for a given keyword using [NewsAPI.org](https://newsapi.org/).  
+Features:
+- Fetch top 3 news articles (can be changed in script).
+- Display title + URL.
+- Shortens long URLs for cleaner output.
+- Logs fetched news to `news_log.txt`.
+- Accepts commands like `show news about AI`.
+
+## Usage
+1. Install dependencies:
+
 <div align="center">
 
 # 🤖 Jarvis AI Assistant
@@ -718,3 +732,4 @@ If you find this project helpful, please consider:
 **© 2025 Varnit Kumar. All rights reserved.**
 
 </div>
+>>>>>>> bd1d8cfda59b40748beac88a96cbf5f19c082e32

backend/auth/recoganize.py

@@ -3,10 +3,16 @@
 import cv2
 import pyautogui as p
 from backend.config import (
-    FACE_TRAINER_PATH, FACE_CASCADE_PATH, FACE_RECOGNITION_CONFIDENCE,
-    CAMERA_INDEX, CAMERA_WIDTH, CAMERA_HEIGHT, FACE_RECOGNITION_NAMES
+    FACE_TRAINER_PATH,
+    FACE_CASCADE_PATH,
+    FACE_RECOGNITION_CONFIDENCE,
+    CAMERA_INDEX,
+    CAMERA_WIDTH,
+    CAMERA_HEIGHT,
+    FACE_RECOGNITION_NAMES,
 )
 
+
 def AuthenticateFace():
 
     flag = ""
@@ -20,20 +26,19 @@ def AuthenticateFace():
 
     font = cv2.FONT_HERSHEY_SIMPLEX  # denotes the font type
 
-
     id = 2  # number of persons you want to Recognize
 
-
     names = FACE_RECOGNITION_NAMES  # names from configuration
 
-
-    cam = cv2.VideoCapture(CAMERA_INDEX, cv2.CAP_DSHOW)  # cv2.CAP_DSHOW to remove warning
+    cam = cv2.VideoCapture(
+        CAMERA_INDEX, cv2.CAP_DSHOW
+    )  # cv2.CAP_DSHOW to remove warning
     cam.set(3, CAMERA_WIDTH)  # set video FrameWidht
     cam.set(4, CAMERA_HEIGHT)  # set video FrameHeight
 
     # Define min window size to be recognized as a face
-    minW = 0.1*cam.get(3)
-    minH = 0.1*cam.get(4)
+    minW = 0.1 * cam.get(3)
+    minH = 0.1 * cam.get(4)
 
     # flag = True
 
@@ -51,16 +56,16 @@ def AuthenticateFace():
             minSize=(int(minW), int(minH)),
         )
 
-        for(x, y, w, h) in faces:
+        for x, y, w, h in faces:
 
             # used to draw a rectangle on any image
-            cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)
+            cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
 
             # to predict on every single image
-            id, accuracy = recognizer.predict(converted_image[y:y+h, x:x+w])
+            id, accuracy = recognizer.predict(converted_image[y : y + h, x : x + w])
 
             # Check if accuracy is less them 100 ==> "0" is perfect match
-            if (accuracy < FACE_RECOGNITION_CONFIDENCE):
+            if accuracy < FACE_RECOGNITION_CONFIDENCE:
                 id = names[id]
                 accuracy = "  {0}%".format(round(100 - accuracy))
                 flag = 1
@@ -69,22 +74,21 @@ def AuthenticateFace():
                 accuracy = "  {0}%".format(round(100 - accuracy))
                 flag = 0
 
-            cv2.putText(img, str(id), (x+5, y-5), font, 1, (255, 255, 255), 2)
-            cv2.putText(img, str(accuracy), (x+5, y+h-5),
-                        font, 1, (255, 255, 0), 1)
+            cv2.putText(img, str(id), (x + 5, y - 5), font, 1, (255, 255, 255), 2)
+            cv2.putText(
+                img, str(accuracy), (x + 5, y + h - 5), font, 1, (255, 255, 0), 1
+            )
 
-        cv2.imshow('camera', img)
+        cv2.imshow("camera", img)
 
-        k = cv2.waitKey(10) & 0xff  # Press 'ESC' for exiting video
+        k = cv2.waitKey(10) & 0xFF  # Press 'ESC' for exiting video
         if k == 27:
             break
         if flag == 1:
             break
-
 
     # Do a bit of cleanup
-    
+
     cam.release()
     cv2.destroyAllWindows()
     return flag
-

backend/auth/sample.py

@@ -1,42 +1,50 @@
 import cv2
 
-cam = cv2.VideoCapture(0, cv2.CAP_DSHOW) #create a video capture object which is helpful to capture videos through webcam
-cam.set(3, 640) # set video FrameWidth
-cam.set(4, 480) # set video FrameHeight
+cam = cv2.VideoCapture(
+    0, cv2.CAP_DSHOW
+)  # create a video capture object which is helpful to capture videos through webcam
+cam.set(3, 640)  # set video FrameWidth
+cam.set(4, 480)  # set video FrameHeight
 
 
-detector = cv2.CascadeClassifier('backend\\auth\\haarcascade_frontalface_default.xml')
-#Haar Cascade classifier is an effective object detection approach
+detector = cv2.CascadeClassifier("backend\\auth\\haarcascade_frontalface_default.xml")
+# Haar Cascade classifier is an effective object detection approach
 
 face_id = input("Enter a Numeric user ID  here:  ")
-#Use integer ID for every new face (0,1,2,3,4,5,6,7,8,9........)
+# Use integer ID for every new face (0,1,2,3,4,5,6,7,8,9........)
 
 print("Taking samples, look at camera ....... ")
-count = 0 # Initializing sampling face count
+count = 0  # Initializing sampling face count
 
 while True:
 
-    ret, img = cam.read() #read the frames using the above created object
-    converted_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #The function converts an input image from one color space to another
+    ret, img = cam.read()  # read the frames using the above created object
+    converted_image = cv2.cvtColor(
+        img, cv2.COLOR_BGR2GRAY
+    )  # The function converts an input image from one color space to another
     faces = detector.detectMultiScale(converted_image, 1.3, 5)
 
-    for (x,y,w,h) in faces:
+    for x, y, w, h in faces:
 
-        cv2.rectangle(img, (x,y), (x+w,y+h), (255,0,0), 2) #used to draw a rectangle on any image
+        cv2.rectangle(
+            img, (x, y), (x + w, y + h), (255, 0, 0), 2
+        )  # used to draw a rectangle on any image
         count += 1
 
-
-        cv2.imwrite("backend\\auth\\samples\\face." + str(face_id) + '.' + str(count) + ".jpg", converted_image[y:y+h,x:x+w])
+        cv2.imwrite(
+            "backend\\auth\\samples\\face." + str(face_id) + "." + str(count) + ".jpg",
+            converted_image[y : y + h, x : x + w],
+        )
         # To capture & Save images into the datasets folder
 
-        cv2.imshow('image', img) #Used to display an image in a window
+        cv2.imshow("image", img)  # Used to display an image in a window
 
-    k = cv2.waitKey(100) & 0xff # Waits for a pressed key
-    if k == 27: # Press 'ESC' to stop
+    k = cv2.waitKey(100) & 0xFF  # Waits for a pressed key
+    if k == 27:  # Press 'ESC' to stop
+        break
+    elif count >= 100:  # Take 50 sample (More sample --> More accuracy)
         break
-    elif count >= 100: # Take 50 sample (More sample --> More accuracy)
-         break
 
 print("Samples taken now closing the program....")
 cam.release()
-cv2.destroyAllWindows()
+cv2.destroyAllWindows()

backend/auth/trainer.py

@@ -1,41 +1,43 @@
 import cv2
 import numpy as np
-from PIL import Image #pillow package
+from PIL import Image  # pillow package
 import os
 
-path = 'backend\\auth\\samples' # Path for samples already taken
+path = "backend\\auth\\samples"  # Path for samples already taken
 
-recognizer = cv2.face.LBPHFaceRecognizer_create() # Local Binary Patterns Histograms
+recognizer = cv2.face.LBPHFaceRecognizer_create()  # Local Binary Patterns Histograms
 detector = cv2.CascadeClassifier("backend\\auth\\haarcascade_frontalface_default.xml")
-#Haar Cascade classifier is an effective object detection approach
+# Haar Cascade classifier is an effective object detection approach
 
 
-def Images_And_Labels(path): # function to fetch the images and labels
+def Images_And_Labels(path):  # function to fetch the images and labels
 
-    imagePaths = [os.path.join(path,f) for f in os.listdir(path)]     
-    faceSamples=[]
+    imagePaths = [os.path.join(path, f) for f in os.listdir(path)]
+    faceSamples = []
     ids = []
 
-    for imagePath in imagePaths: # to iterate particular image path
+    for imagePath in imagePaths:  # to iterate particular image path
 
-        gray_img = Image.open(imagePath).convert('L') # convert it to grayscale
-        img_arr = np.array(gray_img,'uint8') #creating an array
+        gray_img = Image.open(imagePath).convert("L")  # convert it to grayscale
+        img_arr = np.array(gray_img, "uint8")  # creating an array
 
         id = int(os.path.split(imagePath)[-1].split(".")[1])
         faces = detector.detectMultiScale(img_arr)
 
-        for (x,y,w,h) in faces:
-            faceSamples.append(img_arr[y:y+h,x:x+w])
+        for x, y, w, h in faces:
+            faceSamples.append(img_arr[y : y + h, x : x + w])
             ids.append(id)
 
-    return faceSamples,ids
+    return faceSamples, ids
 
-print ("Training faces. It will take a few seconds. Wait ...")
 
-faces,ids = Images_And_Labels(path)
+print("Training faces. It will take a few seconds. Wait ...")
+
+faces, ids = Images_And_Labels(path)
 recognizer.train(faces, np.array(ids))
 
-recognizer.write('backend\\auth\\trainer\\trainer.yml')  # Save the trained model as trainer.yml
+recognizer.write(
+    "backend\\auth\\trainer\\trainer.yml"
+)  # Save the trained model as trainer.yml
 
 print("Model trained, Now we can recognize your face.")
-

backend/command.py

@@ -3,24 +3,33 @@
 import speech_recognition as sr
 import eel
 from backend.config import (
-    TTS_VOICE_ID, TTS_RATE, TTS_VOLUME, TTS_ENGINE,
-    SPEECH_LANGUAGE, SPEECH_TIMEOUT, SPEECH_PHRASE_TIMEOUT, SPEECH_PAUSE_THRESHOLD
+    TTS_VOICE_ID,
+    TTS_RATE,
+    TTS_VOLUME,
+    TTS_ENGINE,
+    SPEECH_LANGUAGE,
+    SPEECH_TIMEOUT,
+    SPEECH_PHRASE_TIMEOUT,
+    SPEECH_PAUSE_THRESHOLD,
 )
 
+
 def speak(text):
     text = str(text)
     engine = pyttsx3.init(TTS_ENGINE)
-    voices = engine.getProperty('voices')
+    voices = engine.getProperty("voices")
     # print(voices)
-    engine.setProperty('voice', voices[TTS_VOICE_ID].id)
+    engine.setProperty("voice", voices[TTS_VOICE_ID].id)
     eel.DisplayMessage(text)
     engine.say(text)
     engine.runAndWait()
-    engine.setProperty('rate', TTS_RATE)
+    engine.setProperty("rate", TTS_RATE)
     eel.receiverText(text)
 
+
 # Expose the Python function to JavaScript
 
+
 def takecommand():
     r = sr.Recognizer()
     with sr.Microphone() as source:
@@ -36,8 +45,7 @@ def takecommand():
         query = r.recognize_google(audio, language=SPEECH_LANGUAGE)
         print(f"User said: {query}\n")
         eel.DisplayMessage(query)
-
-
+
         speak(query)
     except Exception as e:
         print(f"Error: {str(e)}\n")
@@ -46,7 +54,6 @@ def takecommand():
     return query.lower()
 
 
-
 @eel.expose
 def takeAllCommands(message=None):
     if message is None:
@@ -59,36 +66,40 @@ def takeAllCommands(message=None):
         query = message  # If there's a message, use it
         print(f"Message received: {query}")
         eel.senderText(query)
-    
+
     try:
         if query:
             if "open" in query:
                 from backend.feature import openCommand
+
                 openCommand(query)
             elif "send message" in query or "call" in query or "video call" in query:
                 from backend.feature import findContact, whatsApp
+
                 flag = ""
                 Phone, name = findContact(query)
                 if Phone != 0:
                     if "send message" in query:
-                        flag = 'message'
+                        flag = "message"
                         speak("What message to send?")
                         query = takecommand()  # Ask for the message text
                     elif "call" in query:
-                        flag = 'call'
+                        flag = "call"
                     else:
-                        flag = 'video call'
+                        flag = "video call"
                     whatsApp(Phone, query, flag, name)
             elif "on youtube" in query:
                 from backend.feature import PlayYoutube
+
                 PlayYoutube(query)
             else:
                 from backend.feature import chatBot
+
                 chatBot(query)
         else:
             speak("No command was given.")
     except Exception as e:
         print(f"An error occurred: {e}")
         speak("Sorry, something went wrong.")
-    
+
     eel.ShowHood()

backend/config.py

@@ -72,4 +72,4 @@
 YOUTUBE_INTEGRATION_ENABLED = config.youtube_integration_enabled
 AI_CHATBOT_ENABLED = config.ai_chatbot_enabled
 SYSTEM_CONTROL_ENABLED = config.system_control_enabled
-WEB_INTERFACE_ENABLED = config.web_interface_enabled
+WEB_INTERFACE_ENABLED = config.web_interface_enabled